## 【原】mplus数据分析：增长模型潜类别增长模型与增长混合模型再解释

2022-03-09

Latent growth modeling approaches, such as latent class growth analysis (LCGA) and growth mixture modeling (GMM), have been increasingly recognized for their usefulness for identifying homogeneous subpopulations within the larger heterogeneous population and for the identification of meaningful groups or classes of individuals

# 增长混合模型GMM

The conventional growth model can be described as a multilevel, randomeffects model (Raudenbush & Bryk, 2002). According to this framework, intercept and slope vary across individuals and this heterogeneity is captured by random effects

GMM, on the other hand, relaxes this assumption and allows for differences in growth parameters across unobserved subpopulations.

GMM认为轨迹，也就是变量随着时间变化的情况是存在亚组的，而且这些亚组的斜率和截距其实不一样了，这些亚组怎么来呢，是用潜变量表示的，就是潜轨迹类别，叫做latent trajectory classes：

This is accomplished using latent trajectory classes (i.e., categorical latent variables), which allow for different groups of individual growth trajectories to vary around different means (with the same or different forms)

# 潜类别增长模型LCGA

Latent class growth analysis (LCGA) is a special type of GMM, whereby the variance and covariance estimates for the growth factors within each class are assumed to be fixed to zero

# mplus实操

``Model：i s |t1@0  t2@1  t3@2 t4@3;``

`` i s q|t1@0  t2@1  t3@2 t4@3;``

In this example, the syntax i-s@0 fixes all within-class variances to zero, consistent with the LCGA approach. Removing this line will set the variances of I and S as equal across all classes and estimate the variances of the growth parameters

Keeping this in mind, fit indices and tests of model fit should not be the final word in deciding on the number of classes.

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